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Yo'Chameleon (Unofficial)

Project Page | arXiv | HF Datasets

YoChameleon

🦎 Yo’Chameleon: Personalized Vision and Language Generation
Thao Nguyen1, 2, Krishna Kumar Singh2, Jing Shi2, Trung Bui2, Yong Jae Lee1, ¶, Yuheng Li2, ¶
1University of Wisconsin-Madison, 2Adobe Research

(⭑.ᐟ This is Yo'LLaVA meets Chameleon! ⭑.ᐟ)

YoChameleon

Large Multimodal Models (e.g., GPT-4, Gemini, Chameleon) have evolved into powerful tools with millions of users. However, they remain generic models and lack personalized knowledge of specific user concepts. Previous work has explored personalization for text generation, yet it remains unclear how these methods can be adapted to new modalities, such as image generation. In this paper, we introduce Yo'Chameleon, the first attempt to study personalization for large multimodal models. Given 3-5 images of a particular concept, Yo'Chameleon leverages soft-prompt tuning to embed subject-specific information to (i) answer questions about the subject and (ii) recreate pixel-level details to produce images of the subject in new contexts. Yo'Chameleon is trained with (i) a self-prompting optimization mechanism to balance performance across multiple modalities, and (ii) a ``soft-positive" image generation approach to enhanc6e image quality in a few-shot setting. Our qualitative and quantitative analyses reveal that Yo'Chameleon can learn concepts more efficiently using fewer tokens and effectively encode visual attributes, outperforming prompting baselines.

(¶: equal advising)

Table of Contents
  1. Getting Started
  2. Creating Dataset
  3. Train
  4. Test
  5. Evaluation: Detailed Caption, Facial Similarity Scores, CLIP Image-to-Image Similarity, Recognition Accuracy
  6. Acknowledgements

🚀 Getting Started

# Clone the repository
git clone https://github.com/thaoshibe/YoChameleon.git
cd YoChameleon

# Install via pip
conda create -n yochameleon
conda acitvate yochameleon
conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 -c pytorch

pip install -r requirements.txt

# Or run the bash script
bash install.sh

Quick Start

This provide a quick start to train the model with the provided dataset.

1. Download Mini-YoChameleon Data

Here we provide a quick start with a mini-version of the YoChameleon training dataset. Please download it via HuggingFace Datasets.

git lfs install
git clone git@hf.com:datasets/thaoshibe/Mini-YoChameleon-Data

# optional, if you also want to see Yo'LLaVA data
git clone git@hf.com:datasets/thaoshibe/Mini-YoChameleon-Data

2. Train

Run this script to quick train -- You should be able to monitor the training with visualization via WanDB.

python train.py --config config/basic.yaml

🛠️ Creating dataset

#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
#
#   bash scripts provided in `scripts/create_data` folder
#   
#   scripts/create_data
#   ├── retrieve.sh               # retrieve negative examples
#   ├── recognition.sh            # recognition data (100 hard neg, 100 easy neg, & positive)
#   ├── create_soft_positive.sh   # image generation data
#   └── text_only_data.sh         # call GPT-4o for text-only response data
#
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

#
# Remember to check and fill the relative path in the script before running
#

bash scripts/create_soft_positive.sh
Retrieve negative examples 
cd create_training_data/conversation_data
NAMES=("bo" "duck-banana" "marie-cat" "pusheen-cup" "thuytien"
       "brown-duck" "dug" "mydieu" "shiba-black" "tokyo-keyboard"
       "butin" "elephant" "neurips-cup" "shiba-gray" "toodles-galore"
       "cat-cup" "fire" "nha-tho-hanoi" "shiba-sleep" "viruss"
       "chua-thien-mu" "henry" "nha-tho-hcm" "shiba-yellow" "water"
       "ciin" "khanhvy" "oong" "thao" "willinvietnam"
       "denisdang" "lamb" "phuc-map" "thap-but" "yellow-duck"
       "dragon" "mam" "pig-cup" "thap-cham" "yuheng")

for NAME in "${NAMES[@]}"; do
  INPUT_FOLDER="/mnt/localssd/code/data/yochameleon-data/train/${NAME}"
  SAVE_FOLDER="${INPUT_FOLDER}/negative_example"
  LIMIT=5000 # Number of negative examples to retrieve
  echo "Processing folder: ${NAME}"
  
  python create_training_data/retrieve_negative/load_similar_example.py \
    --input_folder $INPUT_FOLDER \
    --save_folder $SAVE_FOLDER \
    --limit $LIMIT \
    --origin "l2"
done
Create recognition data 
cd create_training_data/conversation_data
# List of names or folders to process
NAMES=("bo" "duck-banana" "marie-cat" "pusheen-cup" "thuytien"
       "brown-duck" "dug" "mydieu" "shiba-black" "tokyo-keyboard"
       "butin" "elephant" "neurips-cup" "shiba-gray" "toodles-galore"
       "cat-cup" "fire" "nha-tho-hanoi" "shiba-sleep" "viruss"
       "chua-thien-mu" "henry" "nha-tho-hcm" "shiba-yellow" "water"
       "ciin" "khanhvy" "oong" "thao" "willinvietnam"
       "denisdang" "lamb" "phuc-map" "thap-but" "yellow-duck"
       "dragon" "mam" "pig-cup" "thap-cham" "yuheng")

# Loop through each folder
for NAME in "${NAMES[@]}"; do
  # Define the positive image folder based on the name
  POSITIVE_IMAGE_FOLDER="/mnt/localssd/code/data/yochameleon-data/train/${NAME}"
  
  # Define the negative image folder (assuming it's fixed or can vary similarly)
  NEGATIVE_IMAGE_FOLDER="/mnt/localssd/code/data/yochameleon-data/train/${NAME}/negative_example"
  
  # Define the output file path for the JSON result
  OUTPUT_FILE="/mnt/localssd/code/data/yochameleon-data/train/${NAME}/json"
  
  # Log which folder is being processed
  echo "Processing folder: ${NAME}"
  
  # Execute the Python script with the required arguments
  python create_conversation.py \
    --positive_image_folder "$POSITIVE_IMAGE_FOLDER" \
    --negative_image_folder "$NEGATIVE_IMAGE_FOLDER" \
    --output_file "$OUTPUT_FILE" \
    --limit_positive 5 \
    --limit_negative 100
done
Simple conversation data 
cd create_training_data/dense_caption

# List of names or folders to process -- For human
NAMES=("thuytien" "viruss" "ciin" "khanhvy" "oong" "thao" "willinvietnam" "denisdang" "phuc-map" "yuheng")

# Loop through each folder
for NAME in "${NAMES[@]}"; do
  # Define the positive image folder based on the name
  POSITIVE_IMAGE_FOLDER="/mnt/localssd/code/data/yochameleon-data/train/${NAME}"
  
  # Define the negative image folder (assuming it's fixed or can vary similarly)
  NEGATIVE_IMAGE_FOLDER="/mnt/localssd/code/data/yochameleon-data/train/${NAME}/negative_example"
  
  # Define the output file path for the JSON result
  OUTPUT_FILE="/mnt/localssd/code/data/yochameleon-data/train/${NAME}/json"
  
  # Log which folder is being processed
  echo "Processing folder: ${NAME}"
  
  # Execute the Python script with the required arguments
  python gpt4o-api.py \
    --input_image_folder "$POSITIVE_IMAGE_FOLDER" \
    --prompt_file_path ./system-prompts/text-conversation.txt \
    --output_file "$OUTPUT_FILE" \
    --text_conversation \
    --human \
    --limit 5
done

# List of names or folders to process -- For object
NAMES=("bo" "duck-banana" "marie-cat" "pusheen-cup"
       "brown-duck" "dug" "mydieu" "shiba-black" "tokyo-keyboard"
       "butin" "elephant" "neurips-cup" "shiba-gray" "toodles-galore"
       "cat-cup" "fire" "nha-tho-hanoi" "shiba-sleep"
       "chua-thien-mu" "henry" "nha-tho-hcm" "shiba-yellow" "water"
       "lamb" "thap-but" "yellow-duck"
       "dragon" "mam" "pig-cup" "thap-cham")

# Loop through each folder
for NAME in "${NAMES[@]}"; do
  # Define the positive image folder based on the name
  POSITIVE_IMAGE_FOLDER="/mnt/localssd/code/data/yochameleon-data/train/${NAME}"
  
  # Define the negative image folder (assuming it's fixed or can vary similarly)
  NEGATIVE_IMAGE_FOLDER="/mnt/localssd/code/data/yochameleon-data/train/${NAME}/negative_example"
  
  # Define the output file path for the JSON result
  OUTPUT_FILE="/mnt/localssd/code/data/yochameleon-data/train/${NAME}/json"
  
  # Log which folder is being processed
  echo "Processing folder: ${NAME}"
  
  # Execute the Python script with the required arguments
  python gpt4o-api.py \
    --input_image_folder "$POSITIVE_IMAGE_FOLDER" \
    --prompt_file_path ./system-prompts/text-conversation.txt \
    --output_file "$OUTPUT_FILE" \
    --text_conversation \
    --limit 5
done
Image generation data 
cd create_training_data/retrieve_negative
# List of names or folders to process
NAMES=("bo" "duck-banana" "marie-cat" "pusheen-cup" "thuytien"
       "brown-duck" "dug" "mydieu" "shiba-black" "tokyo-keyboard"
       "butin" "elephant" "neurips-cup" "shiba-gray" "toodles-galore"
       "cat-cup" "fire" "nha-tho-hanoi" "shiba-sleep" "viruss"
       "chua-thien-mu" "henry" "nha-tho-hcm" "shiba-yellow" "water"
       "ciin" "khanhvy" "oong" "thao" "willinvietnam"
       "denisdang" "lamb" "phuc-map" "thap-but" "yellow-duck"
       "dragon" "mam" "pig-cup" "thap-cham" "yuheng")

# Loop through each folder
for NAME in "${NAMES[@]}"; do
  # Define the positive image folder based on the name
  POSITIVE_IMAGE_FOLDER="/mnt/localssd/code/data/yochameleon-data/train/${NAME}"
  
  # Define the output file path for the JSON result
  OUTPUT_FILE="/mnt/localssd/code/data/yochameleon-data/train/${NAME}/json"
  
  # Log which folder is being processed
  echo "Processing folder: ${NAME}"
  
  # Execute the Python script with the required arguments
  python create_conversation_by_ranking.py \
    --input_folder "$POSITIVE_IMAGE_FOLDER" \
    --save_folder "$OUTPUT_FILE" \
    --version image_gen_positive_only \
    --num_of_real_images 100 \ch
    --token_length 16 \
    --spacing 16
done
Soft positive data 
cd create_training_data/retrieve_negative
# List of names or folders to process
# NAMES=("bo" "duck-banana" "marie-cat" "pusheen-cup" "thuytien"
#        "brown-duck" "dug" "mydieu" "shiba-black" "tokyo-keyboard"
#        "butin" "elephant" "neurips-cup" "shiba-gray" "toodles-galore"
#        "cat-cup" "fire" "nha-tho-hanoi" "shiba-sleep" "viruss"
#        "chua-thien-mu" "henry" "nha-tho-hcm" "shiba-yellow" "water"
#        "ciin" "khanhvy" "oong" "thao" "willinvietnam"
#        "denisdang" "lamb" "phuc-map" "thap-but" "yellow-duck"
#        "dragon" "mam" "pig-cup" "thap-cham" "yuheng")

NAMES=("bo" "mam" "thuytien" "viruss" "ciin" "khanhvy" "oong" "thao" "willinvietnam" "denisdang" "phuc-map" "yuheng")
# NAMES=("bo")
# Loop through each folder
for NAME in "${NAMES[@]}"; do
  # Define the positive image folder based on the name
  POSITIVE_IMAGE_FOLDER="/mnt/localssd/code/data/yochameleon-data/train/${NAME}"
  NEGATIVE_IMAGE_FOLDER="/mnt/localssd/code/data/yochameleon-data/train/${NAME}/negative_example"
  # Define the output file path for the JSON result
  OUTPUT_FILE="/mnt/localssd/code/data/yochameleon-data/train/${NAME}/json"
  
  # Log which folder is being processed
  echo "Processing folder: ${NAME}"
  
  # Execute the Python script with the required arguments
  python create_conversation_by_ranking.py \
    --input_folder "$POSITIVE_IMAGE_FOLDER" \
    --save_folder "$OUTPUT_FILE" \
    --version '2000' \
    --num_of_real_images -100 \
    --token_length 16 \
    --spacing 1 \
    --negative_image True \
    --limit_negative 2000
done

YoChameleon

🧑‍🏫 Train

#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
#
#   ATTENTION: PLEASE CHECK/EDIT THE CONFIG FILE BEFORE RUNNING (IF NEEDED)
#
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

python train.py --config config/basic.yaml

If do you NOT want to use the wandb for logging (e.g., for debugging), you can turn off by

python train.py --config config/basic.yaml --no_wandb

Or multiple concept training bash script are given in scripts folder

bash train.sh

🧪 Test

# This test will generated "A photo of <sks>" and saved to `./generated_images`

python test.py --config config/basic.yaml 
# (Or optionally, you can also provide override arguments: e.g., python test.py --config config/basic.yaml --exp_name 1000 --savedir ../ckpt --sks_name "mam")

# Bash script for test

bash scripts/test.sh

📊 Evaluation

Detailed Captions

Detailed captions for each subject in Yo'LLaVA datasets are given in baselines/subject-detailed-captions.json.

For example, the detailed caption for bo is given as follows:

"bo": "<sks> is a charming cinnamon-colored Shiba Inu with cream accents and a cheerful personality, appears in various indoor and outdoor settings—posing on rugs, floors, and sidewalks. Often seen with a playful expression or tongue out, this Shiba enjoys relaxing, smiling for the camera, and is sometimes accompanied by a plush toy or sitting attentively in anticipation of a walk."

Bo

Facial similarity scores

python insightface_verify.py --real_folder /path/to/real/folder --fake_folder /path/to/fake/folder

Or edit the file bash scripts/eval/eval_facial_sim.sh.

facial similarity compute between fake/ real folders 
#!/bin/bash
cd ../evaluation/

EXP_FOLDER="64-5000"
FAKE_FOLDER_BASE="/sensei-fs/users/thaon/code/generated_images"

# Define the real folder
REAL_FOLDER="/mnt/localssd/code/data/yollava-data/train/thao"

# Define an array of fake folders
FAKE_FOLDERS=(
    # Local and aligned folders
    # "/mnt/localssd/code/data/dathao_algined"
    # "/mnt/localssd/code/data/yollava-data/train/khanhvy"
    # "/mnt/localssd/code/data/yollava-data/train/thao/negative_example"
    # Generated image sets using FAKE_FOLDER_BASE
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/1000"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/2000"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/3000"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/4000"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/4050"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/4100"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/4150"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/4200"
)

# Loop through each fake folder and run the Python evaluation script
for FAKE_FOLDER in "${FAKE_FOLDERS[@]}"
do
    echo "Running evaluation with fake folder: $FAKE_FOLDER"
    python insightface_verify.py --real_folder "$REAL_FOLDER" --fake_folder "$FAKE_FOLDER"
done

echo "All evaluations completed!"

CLIP Image-to-Image Similarity

python clip_image_similarity.py --real_folder /path/to/real/folder --fake_folder /path/to/fake/folder
clip similarity score between fake/ real folders 
#!/bin/bash
cd ../evaluation/

EXP_FOLDER="64-5000"
FAKE_FOLDER_BASE="/sensei-fs/users/thaon/code/generated_images"

# Define the real folder
REAL_FOLDER="/mnt/localssd/code/data/yollava-data/train/thao"

# Define an array of fake folders
FAKE_FOLDERS=(
    # Local and aligned folders
    # "/mnt/localssd/code/data/dathao_algined"
    # "/mnt/localssd/code/data/yollava-data/train/khanhvy"
    # "/mnt/localssd/code/data/yollava-data/train/thao/negative_example"
    # Generated image sets using FAKE_FOLDER_BASE
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/1000"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/2000"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/3000"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/4000"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/4050"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/4100"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/4150"
    "${FAKE_FOLDER_BASE}/${EXP_FOLDER}/4200"
)

# Loop through each fake folder and run the Python evaluation script
for FAKE_FOLDER in "${FAKE_FOLDERS[@]}"
do
    echo "Running evaluation with fake folder: $FAKE_FOLDER"
    python clip_image_similarity.py --real_folder "$REAL_FOLDER" --fake_folder "$FAKE_FOLDER"
done

echo "All evaluations completed!"

Recognition Accuracy

python evaluation/recognition.py --config ./config/recog.yaml --sks_name "thao" --iteration 15

🖊️ Citation

@inproceedings{yochameleon,
  author = {Thao Nguyen and Krishna Kumar Singh and Jing Shi and Trung Bui and Yong Jae Lee and Yuheng Li},
  title = {Yo\textquotesingle Chameleon: Personalized Vision and Language Generation},
  year = {2025},
  booktitle = {Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)}
}

@inproceedings{yollava,
 author = {Nguyen, Thao and Liu, Haotian and Li, Yuheng and Cai, Mu and Ojha, Utkarsh and Lee, Yong Jae},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {Yo\textquotesingle LLaVA: Your Personalized Language and Vision Assistant},
 year = {2024}
}

🤗 Acknowledgements

This project will not be possible without the following open-source projects:

⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣀⣀⣀⠀⠀⠀⢀⡤⠤⠤⣄⠀⣀⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⣀⣀⠤⢴⣴⠒⠉⠹⣴⣏⠀⠀⠀⡀⠈⢇⠀⠀⣼⠀⠀⠀⠘⣶⠇⠀⢨⢃⡾⠓⠲⢤⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⣀⠤⠔⠒⠙⣯⣇⠀⠈⣿⣇⠀⠀⣿⣿⣿⠀⠀⣷⠀⠘⡄⠀⣿⠀⠀⠀⠀⢹⠀⠀⢸⡏⠇⠀⢀⠇⣀⠤⠒⠒⠤⣄ ⢰⡖⠉⠀⠀⠀⠀⣀⣸⣿⠀⠀⠉⠉⠀⠀⢸⠁⣿⠀⠈⠉⠁⠀⢱⠀⣿⠀⠀⣦⠀⠀⠀⠀⣿⡸⠀⠀⠘⠉⠀⠀⣀⣤⣴⠟ ⢼⢣⣀⣴⡀⠀⠘⡿⠏⠗⡆⠀⠠⣶⡆⠀⠸⡄⡏⠀⠀⣶⣷⠀⠀⢧⣿⠀⠀⣿⡆⠀⠀⢸⣿⠃⠀⢰⡄⠀⠐⡿⠛⠋⠀⠀ ⠘⢿⡿⢿⣧⠀⠀⢳⠀⢸⠸⠀⠀⢹⣧⢀⣀⣷⣧⣤⣤⠛⣏⣦⣤⣾⣿⢦⣤⣿⢸⣄⣀⣼⡏⠀⢠⡟⡇⠀⠀⡇⠀⠀⠀⠀ ⠀⠀⠀⠀⢏⢇⠀⠀⣣⠀⣆⣷⣶⣿⣿⡿⠿⠿⢷⡿⠟⣠⠟⠋⠛⢿⡛⠛⠿⡼⠿⠿⢿⣿⣿⣶⠞⡅⢸⠀⠀⢸⠀⠀⠀⠀ ⠀⠀⠀⠀⠘⣾⣿⣿⠇⢠⣟⠉⠙⠷⡿⠀⠀⠀⢸⢀⡼⠁⠀⣀⠀⠀⠹⡄⡼⡇⠀⠀⡜⣸⡏⠙⠢⣧⣾⣦⣀⢸⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠈⠀⠀⠀⢿⣿⣷⣦⡀⠀⠀⠀⠀⣇⡾⠀⠀⣼⣿⢷⠀⠀⢻⢱⠀⠀⢀⣿⡿⠀⠀⢠⠋⢻⡿⠿⣏⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠳⣿⣿⠆⠀⠀⢸⡏⡇⠀⠀⡏⡟⡟⠀⠀⢸⡸⠀⠀⢸⣿⠃⠀⠀⡜⡰⢩⠃⠀⠈⣱⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢹⠀⠀⠀⢸⠀⡇⠀⠀⠙⠋⠀⠀⢀⡏⡇⠀⠀⠘⠋⠀⠀⣰⣱⢣⠇⠀⠀⣰⠃⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡘⡎⠀⠀⠀⡏⣿⣧⡀⠀⠀⠀⠀⢀⣾⣷⡇⠀⠀⠀⠀⠀⢠⣯⣧⣾⣦⣄⣰⠃⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣧⣧⣤⣶⣶⠃⠘⢿⣿⣷⣶⣶⣾⠟⠉⣿⣿⣦⣄⣀⣠⣴⢏⣽⠋⠉⠙⢿⠁⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠛⠛⠛⠋⠁⠀⠀⠀⠉⠉⠉⠉⠀⠀⠀⠈⠛⠻⠿⠟⠋⠁⣿⣿⣦⣀⣀⡼⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⠛⠛⠛⠁⠀⠀⠀⠀⠀⠀⠀

About

🦎 Yo'Chameleon: Your Personalized Chameleon (CVPR 2025)

thaoshibe.github.io/YoChameleon/

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personalization chameleon lmms cvpr personalized large-language-models llms personalized-generation large-multimodal-models cvpr2025

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